Data

mydata <- read.csv('/Users/xbasra/Documents/Data/Airway_Clustering/Original_Data/AirwayDiseasePhenotypingDataSets5/WSAS_AndOLIN_AirwayDiseasePhenotyping.csv')
result_airway_uft_DEC <- read.csv('/Users/xbasra/Documents/Data/Airway_Clustering/Intermediate/CSV_output_data/result_airway_uft_DEC.csv')

#Libraries

library(tidyverse)
library(treeClust)
library(Rtsne)
library(ggplot2)
library(factoextra)
library(plotly)
library(cowplot)

Functions

Get_Binary_data <- function(Data){
  # checking the data format
  if (!is.data.frame(Data)){
    stop("The Data is not in dataframe format")
  }
  # droping lopnr
  # Selecting Specific Variables under the selection creteria. other_CVD all yes
  drops <- c("lopnr","kohort","urbanization","rhinitis_ever","wheeze_ever")
  Airway2 <- Data[ , !(names(Data) %in% drops)]
  
  # this is only to adjust the smoking varaible to three categories Basically replacing number 0 with Never-Smoker
  Airway2 <- Airway2 %>% mutate(ever_smoker20py=replace(ever_smoker20py, ever_smoker20py==0, 'Never-smoker')) %>% as.data.frame()
  # replacing ever_Somoker variable with integer values
  Airway2$ever_smoker20py <- ifelse(Airway2$ever_smoker20py == 'Never-smoker',0, ifelse(Airway2$ever_smoker20py == '<=20 packyears',1, ifelse(Airway2$ever_smoker20py == '>20 packyears',2,0)))
  # converting the ever_somke to integer variable
  Airway2$ever_smoker20py <- as.integer(Airway2$ever_smoker20py)
  
  Airway2 <- mutate(Airway2, Longstanding_cough = if_else(Longstanding_cough == "Yes", 1L, 0L),
                    Sputum_production = if_else(Sputum_production == "Yes", 1L,0L),
                    Chronic_productive_cough = if_else(Chronic_productive_cough == "Yes", 1L, 0L),
                    Recurrent_wheeze = if_else(Recurrent_wheeze == "Yes", 1L,0L),
                    exp_dust_work = if_else(exp_dust_work == "Yes",1L,0L),
                    gender = if_else(gender == "male", 1L,0L))
  return(Airway2)
}
## Density plot
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 

UFT + DEC method visulization

result_Air_tree_UFT_DEC <- Airway2
Error: object 'Airway2' not found

Density plot shoiwing the age distribution for each cluster

#result_Airway_tree$age <- mydata$age_class3 # adding age variable from the original data
#ggplot(data = result_Airway_tree, aes (x = result_Airway_tree$age, fill = result_Airway_tree$clusters )) +  geom_bar(position = "dodge2")
result_Air_tree_UFT_DEC$clusters <- as.factor(result_Air_tree_UFT_DEC$clusters)
age_g <- ggplot(result_Air_tree_UFT_DEC, aes(F107))
age_p <- age_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="Age of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="Age",
         fill="# Clusters")
#p <- ggplot(data = result_Airway_tree, aes (x = F107, fill = clusters )) +  geom_bar(position = "dodge2")
ggplotly(age_p)
bmi_p <- dist_plot_clust(original_data = result_Air_tree_UFT_DEC, selected_variable = BMI)
bmi_p

The tree-based distance approach

fev_g <- ggplot(result_Air_tree_UFT_DEC, aes(fev1pp_olin))
fev_p <- fev_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="fev1pp_olin of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="fev1pp_olin",
         fill="# Clusters")
fvc_g <- ggplot(result_Air_tree_UFT_DEC, aes(fev1pp_olin))
fvc_p <- fvc_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="fev1pp_olin of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="fev1pp_olin",
         fill="# Clusters")
plot_grid(fev_p, fvc_p, labels = "auto")

Histogram plots

# Histogram on a Continuous (Numeric) Variable
g <- ggplot(result_Air_tree_UFT_DEC, aes(F107)) + scale_fill_brewer(palette = "Spectral")
k <- g + geom_histogram(aes(fill=factor(clusters)), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters")  
ggplotly(k)
s <- g + geom_histogram(aes(fill=factor(clusters)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 
ggplotly(s)

Box plot

g <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), F107))
l <- g + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="Age")
ggplotly(l)
colnames(num_result_dec)
[1] "F107"                "fev1pp_olin"         "fvcpp_olin"          "BMI"                 "post_fev1_fvc_ratio"
[6] "delta_fev1"          "clusters"           
g_2 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), fev1pp_olin))
l_2 <- g_2 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="fev1pp_olin")
ggplotly(l_2)
g_3 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), BMI))
l_3 <- g_3 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="BMI")
ggplotly(l_3)
g_4 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), post_fev1_fvc_ratio))
l_4 <- g_4 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="post_fev1_fvc_ratio")
ggplotly(l_4)
g_5 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), delta_fev1))
l_5 <- g_5 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="delta_fev1")
ggplotly(l_5)
g_6 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), fvcpp_olin))
l_6 <- g_6 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="fvcpp_olin")
ggplotly(l_6)

Box plot stratified by categorical variables

categorical variables vis

# categorical variables vis
k <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$Longstanding_cough), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k) 
# categorical variables vis
k_2 <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$dyspneaMRC), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k_2) 
k_3 <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$ever_smoker20py), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k_3) 

parallel box plots

parallel coordinate plot

result_Airway_tree$ever_smoker20py <- as.factor(result_Airway_tree$ever_smoker20py)
result_Airway_tree$dyspneaMRC <- as.factor(result_Airway_tree$dyspneaMRC)
table_tree <- tableby(clusters ~ ., data = as.list(result_Airway_tree))
summary(table_tree, title = "Charachtaristcs of Clusters")  
write.csv(result_Airway_tree,'/Users/xbasra/Documents/Data/Airway_Clustering/Intermediate/CSV_output_data/result_Airway_tree_d1_hc.csv')

Charachtiristic Analysis

catdes(result_Air_tree_UFT_DEC_2, 23)

Link between the cluster variable and the categorical variables (chi-square test)
=================================================================================
                              p.value df
dyspneaMRC               0.000000e+00 20
ever_smoker20py          3.965147e-54 10
asthmatic_wheeze         3.188526e-43  5
wheezeSB                 1.453088e-40  5
Recurrent_wheeze         3.537243e-36  5
Sputum_production        2.583850e-28  5
Airway_medicine_use2     6.981025e-27  5
current_rhinitis         3.150519e-26  5
fam_asthma_allergy       2.849254e-19  5
Chronic_productive_cough 3.138500e-17  5
exp_dust_work            1.813407e-13  5
Longstanding_cough       4.346748e-07  5
gender                   4.624488e-07  5
exacerbations            1.718670e-06  5
IgEorSPT                 3.875284e-06  5
fam_bronch_emphys        1.109370e-04  5

Description of each cluster by the categories
=============================================
$`0`
                            Cla/Mod    Mod/Cla     Global       p.value     v.test
dyspneaMRC=0               50.22901 100.000000 52.1081941 2.721225e-117  23.023345
ever_smoker20py=0          40.25357  77.203647 50.1988862  3.218041e-31  11.621142
Sputum_production=0        32.78689  66.869301 53.3810660  9.153538e-09   5.745711
current_rhinitis=1         30.31625  84.498480 72.9514718  1.325154e-08   5.682789
Recurrent_wheeze=0         29.59918  87.537994 77.4065235  9.970614e-08   5.327259
fam_asthma_allergy=1       33.52713  52.583587 41.0501193  8.953519e-07   4.913346
Chronic_productive_cough=0 28.82012  90.577508 82.2593477  1.473043e-06   4.814858
IgEorSPT=0                 33.75315  40.729483 31.5831344  4.266735e-05   4.092540
wheezeSB=0                 27.26473  94.224924 90.4534606  5.039782e-03   2.804480
Longstanding_cough=0       28.15217  78.723404 73.1901352  7.665100e-03   2.666476
fam_bronch_emphys=0        27.17584  93.009119 89.5783612  1.508110e-02   2.430426
dyspneaMRC=3                0.00000   0.000000  0.7955449  4.748780e-02  -1.981924
fam_bronch_emphys=1        17.55725   6.990881 10.4216388  1.508110e-02  -2.430426
Longstanding_cough=1       20.77151  21.276596 26.8098648  7.665100e-03  -2.666476
wheezeSB=1                 15.83333   5.775076  9.5465394  5.039782e-03  -2.804480
IgEorSPT=1                 22.67442  59.270517 68.4168656  4.266735e-05  -4.092540
Chronic_productive_cough=1 13.90135   9.422492 17.7406523  1.473043e-06  -4.814858
fam_asthma_allergy=0       21.05263  47.416413 58.9498807  8.953519e-07  -4.913346
dyspneaMRC=2                0.00000   0.000000  3.8981702  2.476610e-07  -5.159462
Recurrent_wheeze=1         14.43662  12.462006 22.5934765  9.970614e-08  -5.327259
ever_smoker20py=1          16.66667  21.580547 33.8902148  1.946182e-08  -5.616718
current_rhinitis=0         15.00000  15.501520 27.0485282  1.325154e-08  -5.682789
Sputum_production=1        18.60068  33.130699 46.6189340  9.153538e-09  -5.745711
ever_smoker20py=2           2.00000   1.215805 15.9108990  2.028060e-23  -9.971662
dyspneaMRC=1                0.00000   0.000000 42.8003182  3.629607e-99 -21.137065

$`1`
                          Cla/Mod    Mod/Cla     Global       p.value     v.test
dyspneaMRC=0           49.6183206 99.6932515 52.1081941 3.701293e-113  22.606965
fam_asthma_allergy=0   33.7381916 76.6871166 58.9498807  1.022896e-14   7.736377
asthmatic_wheeze=0     28.8443171 92.6380368 83.2935561  2.248518e-08   5.591704
Airway_medicine_use2=0 28.5152409 88.9570552 80.9069212  7.888232e-06   4.468195
gender=1               31.6749585 58.5889571 47.9713604  8.427916e-06   4.454015
IgEorSPT=1             29.3023256 77.3006135 68.4168656  4.564601e-05   4.076870
current_rhinitis=0     33.8235294 35.2760736 27.0485282  1.360419e-04   3.815256
wheezeSB=0             27.3526825 95.3987730 90.4534606  1.897122e-04   3.732336
exacerbations=0        26.5508685 98.4662577 96.1813842  8.086012e-03   2.648457
ever_smoker20py=1      30.5164319 39.8773006 33.8902148  8.545864e-03   2.629706
ever_smoker20py=2      33.5000000 20.5521472 15.9108990  9.237298e-03   2.603145
dyspneaMRC=3            0.0000000  0.0000000  0.7955449  4.905321e-02  -1.968129
exacerbations=1        10.4166667  1.5337423  3.8186158  8.086012e-03  -2.648457
wheezeSB=1             12.5000000  4.6012270  9.5465394  1.897122e-04  -3.732336
current_rhinitis=1     23.0098146 64.7239264 72.9514718  1.360419e-04  -3.815256
IgEorSPT=0             18.6397985 22.6993865 31.5831344  4.564601e-05  -4.076870
gender=0               20.6422018 41.4110429 52.0286396  8.427916e-06  -4.454015
ever_smoker20py=0      20.4437401 39.5705521 50.1988862  8.116151e-06  -4.462097
Airway_medicine_use2=1 15.0000000 11.0429448 19.0930788  7.888232e-06  -4.468195
dyspneaMRC=2            0.0000000  0.0000000  3.8981702  2.913257e-07  -5.128974
asthmatic_wheeze=1     11.4285714  7.3619632 16.7064439  2.248518e-08  -5.591704
fam_asthma_allergy=1   14.7286822 23.3128834 41.0501193  1.022896e-14  -7.736377
dyspneaMRC=1            0.1858736  0.3067485 42.8003182  2.167220e-95 -20.722587

$`2`
                          Cla/Mod    Mod/Cla    Global      p.value     v.test
dyspneaMRC=1           31.7843866 99.4186047 42.800318 1.217422e-69  17.639869
wheezeSB=1             51.6666667 36.0465116  9.546539 1.514228e-26  10.663125
asthmatic_wheeze=1     39.0476190 47.6744186 16.706444 2.359453e-25  10.404721
Recurrent_wheeze=1     28.1690141 46.5116279 22.593477 5.459479e-14   7.520434
Airway_medicine_use2=1 29.5833333 41.2790698 19.093079 2.417241e-13   7.323423
fam_asthma_allergy=1   19.9612403 59.8837209 41.050119 9.238378e-08   5.341101
exp_dust_work=1        20.6611570 43.6046512 28.878282 9.287700e-06   4.433125
current_rhinitis=1     15.3762268 81.9767442 72.951472 3.218026e-03   2.946106
exacerbations=1        29.1666667  8.1395349  3.818616 4.395297e-03   2.848304
fam_bronch_emphys=1    21.3740458 16.2790698 10.421639 1.049150e-02   2.559195
fam_bronch_emphys=0    12.7886323 83.7209302 89.578361 1.049150e-02  -2.559195
ever_smoker20py=2       8.0000000  9.3023256 15.910899 7.812264e-03  -2.660078
exacerbations=0        13.0686518 91.8604651 96.181384 4.395297e-03  -2.848304
current_rhinitis=0      9.1176471 18.0232558 27.048528 3.218026e-03  -2.946106
dyspneaMRC=2            0.0000000  0.0000000  3.898170 6.344133e-04  -3.416458
exp_dust_work=0        10.8501119 56.3953488 71.121718 9.287700e-06  -4.433125
fam_asthma_allergy=0    9.3117409 40.1162791 58.949881 9.238378e-08  -5.341101
Airway_medicine_use2=0  9.9311701 58.7209302 80.906921 2.417241e-13  -7.323423
Recurrent_wheeze=0      9.4552929 53.4883721 77.406523 5.459479e-14  -7.520434
asthmatic_wheeze=0      8.5959885 52.3255814 83.293556 2.359453e-25 -10.404721
wheezeSB=0              9.6745822 63.9534884 90.453461 1.514228e-26 -10.663125
dyspneaMRC=0            0.1526718  0.5813953 52.108194 9.208062e-60 -16.304238

$`3`
                              Cla/Mod     Mod/Cla    Global      p.value     v.test
dyspneaMRC=1               37.5464684 100.0000000 42.800318 1.101319e-86  19.733987
Recurrent_wheeze=0         20.0411100  96.5346535 77.406523 3.846725e-16   8.143292
exp_dust_work=0            20.2460850  89.6039604 71.121718 9.730541e-12   6.810433
wheezeSB=0                 17.6781003  99.5049505 90.453461 6.422276e-09   5.805373
asthmatic_wheeze=0         18.4336199  95.5445545 83.293556 1.267720e-08   5.690360
Sputum_production=0        20.7153502  68.8118812 53.381066 1.273231e-06   4.843887
Chronic_productive_cough=0 18.0851064  92.5742574 82.259348 6.492764e-06   4.509673
gender=0                   20.4892966  66.3366337 52.028640 7.871683e-06   4.468644
current_rhinitis=1         18.7568157  85.1485149 72.951472 8.618673e-06   4.449210
Airway_medicine_use2=0     18.0924287  91.0891089 80.906921 1.850511e-05   4.282204
Longstanding_cough=0       17.5000000  79.7029703 73.190135 2.054107e-02   2.316315
IgEorSPT=0                 19.6473552  38.6138614 31.583134 2.075420e-02   2.312426
fam_bronch_emphys=0        16.7850799  93.5643564 89.578361 3.648168e-02   2.091518
fam_bronch_emphys=1         9.9236641   6.4356436 10.421639 3.648168e-02  -2.091518
IgEorSPT=1                 14.4186047  61.3861386 68.416866 2.075420e-02  -2.312426
Longstanding_cough=1       12.1661721  20.2970297 26.809865 2.054107e-02  -2.316315
ever_smoker20py=2           8.5000000   8.4158416 15.910899 8.043778e-04  -3.351284
dyspneaMRC=2                0.0000000   0.0000000  3.898170 1.555541e-04  -3.782031
Airway_medicine_use2=1      7.5000000   8.9108911 19.093079 1.850511e-05  -4.282204
current_rhinitis=0          8.8235294  14.8514851 27.048528 8.618673e-06  -4.449210
gender=1                   11.2769486  33.6633663 47.971360 7.871683e-06  -4.468644
Chronic_productive_cough=1  6.7264574   7.4257426 17.740652 6.492764e-06  -4.509673
Sputum_production=1        10.7508532  31.1881188 46.618934 1.273231e-06  -4.843887
asthmatic_wheeze=1          4.2857143   4.4554455 16.706444 1.267720e-08  -5.690360
wheezeSB=1                  0.8333333   0.4950495  9.546539 6.422276e-09  -5.805373
exp_dust_work=1             5.7851240  10.3960396 28.878282 9.730541e-12  -6.810433
Recurrent_wheeze=1          2.4647887   3.4653465 22.593477 3.846725e-16  -8.143292
dyspneaMRC=0                0.0000000   0.0000000 52.108194 1.624404e-74 -18.263214

$`4`
                              Cla/Mod Mod/Cla     Global      p.value    v.test
dyspneaMRC=2               100.000000 76.5625  3.8981702 3.397242e-75 18.348440
dyspneaMRC=3               100.000000 15.6250  0.7955449 5.785394e-14  7.512850
Recurrent_wheeze=1          13.380282 59.3750 22.5934765 8.414420e-11  6.492999
Airway_medicine_use2=1      14.166667 53.1250 19.0930788 4.061192e-10  6.251658
asthmatic_wheeze=1          14.285714 46.8750 16.7064439 7.832647e-09  5.772021
dyspneaMRC=4               100.000000  7.8125  0.3977725 2.938841e-07  5.127327
Chronic_productive_cough=1  12.556054 43.7500 17.7406523 7.068792e-07  4.959464
Sputum_production=1          8.191126 75.0000 46.6189340 2.631431e-06  4.697672
Longstanding_cough=1         9.495549 50.0000 26.8098648 5.416533e-05  4.036886
wheezeSB=1                  13.333333 25.0000  9.5465394 2.113204e-04  3.705084
exacerbations=1             18.750000 14.0625  3.8186158 5.319585e-04  3.464125
ever_smoker20py=2           10.500000 32.8125 15.9108990 5.947443e-04  3.434000
fam_bronch_emphys=1          9.923664 20.3125 10.4216388 1.595813e-02  2.409872
gender=0                     6.422018 65.6250 52.0286396 2.553771e-02  2.233170
ever_smoker20py=0            3.803487 37.5000 50.1988862 3.788107e-02 -2.076139
gender=1                     3.648425 34.3750 47.9713604 2.553771e-02 -2.233170
fam_bronch_emphys=0          4.529307 79.6875 89.5783612 1.595813e-02 -2.409872
exacerbations=0              4.549214 85.9375 96.1813842 5.319585e-04 -3.464125
wheezeSB=0                   4.221636 75.0000 90.4534606 2.113204e-04 -3.705084
Longstanding_cough=0         3.478261 50.0000 73.1901352 5.416533e-05 -4.036886
Sputum_production=0          2.384501 25.0000 53.3810660 2.631431e-06 -4.697672
Chronic_productive_cough=0   3.481625 56.2500 82.2593477 7.068792e-07 -4.959464
asthmatic_wheeze=0           3.247373 53.1250 83.2935561 7.832647e-09 -5.772021
Airway_medicine_use2=0       2.949853 46.8750 80.9069212 4.061192e-10 -6.251658
Recurrent_wheeze=0           2.672148 40.6250 77.4065235 8.414420e-11 -6.492999
dyspneaMRC=1                 0.000000  0.0000 42.8003182 8.443431e-17 -8.324852
dyspneaMRC=0                 0.000000  0.0000 52.1081941 5.456106e-22 -9.639296

$`5`
                             Cla/Mod    Mod/Cla    Global      p.value     v.test
dyspneaMRC=1               30.483271 100.000000 42.800318 2.008402e-68  17.480750
ever_smoker20py=2          37.500000  45.731707 15.910899 4.855441e-23   9.884594
Sputum_production=1        22.354949  79.878049 46.618934 1.086482e-20   9.327255
current_rhinitis=0         27.352941  56.707317 27.048528 5.817225e-18   8.636085
Chronic_productive_cough=1 24.215247  32.926829 17.740652 3.740118e-07   5.081730
exp_dust_work=1            19.008264  42.073171 28.878282 1.072847e-04   3.873497
fam_asthma_allergy=0       15.654521  70.731707 58.949881 8.720079e-04   3.328865
Longstanding_cough=1       18.397626  37.804878 26.809865 9.529575e-04   3.304059
Recurrent_wheeze=1         18.309859  31.707317 22.593477 3.826252e-03   2.892142
asthmatic_wheeze=0         14.135626  90.243902 83.293556 7.612210e-03   2.668803
wheezeSB=0                 13.808267  95.731707 90.453461 8.488939e-03   2.631978
fam_bronch_emphys=1        19.083969  15.243902 10.421639 3.835727e-02   2.071016
fam_bronch_emphys=0        12.344583  84.756098 89.578361 3.835727e-02  -2.071016
wheezeSB=1                  5.833333   4.268293  9.546539 8.488939e-03  -2.631978
asthmatic_wheeze=1          7.619048   9.756098 16.706444 7.612210e-03  -2.668803
Recurrent_wheeze=0         11.510791  68.292683 77.406523 3.826252e-03  -2.892142
Longstanding_cough=0       11.086957  62.195122 73.190135 9.529575e-04  -3.304059
dyspneaMRC=2                0.000000   0.000000  3.898170 9.167570e-04  -3.314901
fam_asthma_allergy=1        9.302326  29.268293 41.050119 8.720079e-04  -3.328865
exp_dust_work=0            10.626398  57.926829 71.121718 1.072847e-04  -3.873497
Chronic_productive_cough=0 10.638298  67.073171 82.259348 3.740118e-07  -5.081730
current_rhinitis=1          7.742639  43.292683 72.951472 5.817225e-18  -8.636085
Sputum_production=0         4.918033  20.121951 53.381066 1.086482e-20  -9.327255
ever_smoker20py=0           4.437401  17.073171 50.198886 5.496135e-21  -9.399235
dyspneaMRC=0                0.000000   0.000000 52.108194 5.463938e-59 -16.195066


Link between the cluster variable and the quantitative variables
================================================================
                          Eta2      P-value
F107                0.26485075 4.239122e-81
post_fev1_fvc_ratio 0.21669763 5.400125e-64
BMI                 0.19683212 2.976765e-57
fev1pp_olin         0.18456569 3.545059e-53
fvcpp_olin          0.08861573 1.982564e-23

Description of each cluster by quantitative variables
=====================================================
$`0`
                        v.test Mean in category Overall mean sd in category  Overall sd      p.value
fev1pp_olin          10.292879      104.0477360   97.0359610    10.40966049 14.37506759 7.587340e-25
post_fev1_fvc_ratio  10.079407        0.8198007    0.7833509     0.04789693  0.07630947 6.813580e-24
fvcpp_olin            7.357658      100.5008289   96.1374840    10.41059758 12.51407379 1.871643e-13
BMI                  -8.629830       24.9691302   26.8305285     3.49667744  4.55151386 6.144339e-18
F107                -14.762485       41.6591598   52.0779492    12.61016543 14.89280709 2.556826e-49

$`1`
                       v.test Mean in category Overall mean sd in category  Overall sd      p.value
F107                12.154907       60.7097106   52.0779492    10.29462369 14.89280709 5.402324e-34
fev1pp_olin         -3.459618       94.6645367   97.0359610    13.77340852 14.37506759 5.409421e-04
post_fev1_fvc_ratio -8.442971        0.7526292    0.7833509     0.06933842  0.07630947 3.093730e-17

$`2`
                       v.test Mean in category Overall mean sd in category  Overall sd      p.value
BMI                 12.281422       30.7920350   26.8305285      5.2725423  4.55151386 1.139765e-34
post_fev1_fvc_ratio  4.164255        0.8058711    0.7833509      0.0611382  0.07630947 3.123706e-05
F107                -2.308584       49.6413842   52.0779492     15.2128067 14.89280709 2.096670e-02
fvcpp_olin          -4.255465       92.3634873   96.1374840     11.8850546 12.51407379 2.086155e-05

$`3`
                       v.test Mean in category Overall mean sd in category  Overall sd      p.value
post_fev1_fvc_ratio  5.783553        0.8118105    0.7833509     0.05490749  0.07630947 7.313915e-09
fev1pp_olin          5.693330      102.3135068   97.0359610    11.66673977 14.37506759 1.245850e-08
fvcpp_olin           2.447555       98.1125743   96.1374840    10.88322735 12.51407379 1.438290e-02
F107                -2.882506       49.3097170   52.0779492    15.30998309 14.89280709 3.945255e-03
BMI                 -3.344518       25.8489047   26.8305285     3.74787167  4.55151386 8.242572e-04

$`4`
                       v.test Mean in category Overall mean sd in category  Overall sd      p.value
BMI                  7.287718       30.8714728   26.8305285      6.4749574  4.55151386 3.152481e-13
F107                 4.565343       60.3609056   52.0779492     13.2996419 14.89280709 4.986787e-06
post_fev1_fvc_ratio -5.230032        0.7347305    0.7833509      0.1163171  0.07630947 1.694806e-07
fvcpp_olin          -6.297449       86.5368750   96.1374840     14.4471071 12.51407379 3.025840e-10
fev1pp_olin         -7.557704       83.8006183   97.0359610     19.8280646 14.37506759 4.102462e-14

$`5`
                       v.test Mean in category Overall mean sd in category  Overall sd      p.value
F107                 5.969072        58.553500   52.0779492    10.93713274 14.89280709 2.386063e-09
fvcpp_olin          -3.872012        92.607857   96.1374840    12.97950572 12.51407379 1.079406e-04
fev1pp_olin         -8.335613        88.307438   97.0359610    14.60218120 14.37506759 7.709798e-17
post_fev1_fvc_ratio -9.310101         0.731599    0.7833509     0.08881338  0.07630947 1.277106e-20
---
title: "Results DT + HC and UFT + kmeans"
output:
  html_notebook: default
  pdf_document: default
---

```{r loadlib, include=FALSE}
#mydata <- read.csv('/Users/xbasra/Documents/Data/Clustering/Data/Airway Disease Phenotyping Data Sets4/WSAS And OLIN Airway Disease Phenotyping.csv')
library(ggthemes)
#library(FactoMineR)
library(factoextra)
library(arsenal)
library(plotly)
library(cowplot)
```

# Data
```{r}
mydata <- read.csv('/Users/xbasra/Documents/Data/Airway_Clustering/Original_Data/AirwayDiseasePhenotypingDataSets5/WSAS_AndOLIN_AirwayDiseasePhenotyping.csv')
result_airway_uft_DEC <- read.csv('/Users/xbasra/Documents/Data/Airway_Clustering/Intermediate/CSV_output_data/result_airway_uft_DEC.csv')

```
#Libraries
```{r}
library(tidyverse)
library(treeClust)
library(Rtsne)
library(ggplot2)
library(factoextra)
library(plotly)
library(cowplot)
```

# Functions
```{r}
Get_Binary_data <- function(Data){
  # checking the data format
  if (!is.data.frame(Data)){
    stop("The Data is not in dataframe format")
  }
  # droping lopnr
  # Selecting Specific Variables under the selection creteria. other_CVD all yes
  drops <- c("lopnr","kohort","urbanization","rhinitis_ever","wheeze_ever")
  Airway2 <- Data[ , !(names(Data) %in% drops)]
  
  # this is only to adjust the smoking varaible to three categories Basically replacing number 0 with Never-Smoker
  Airway2 <- Airway2 %>% mutate(ever_smoker20py=replace(ever_smoker20py, ever_smoker20py==0, 'Never-smoker')) %>% as.data.frame()
  # replacing ever_Somoker variable with integer values
  Airway2$ever_smoker20py <- ifelse(Airway2$ever_smoker20py == 'Never-smoker',0, ifelse(Airway2$ever_smoker20py == '<=20 packyears',1, ifelse(Airway2$ever_smoker20py == '>20 packyears',2,0)))
  # converting the ever_somke to integer variable
  Airway2$ever_smoker20py <- as.integer(Airway2$ever_smoker20py)
  
  Airway2 <- mutate(Airway2, Longstanding_cough = if_else(Longstanding_cough == "Yes", 1L, 0L),
                    Sputum_production = if_else(Sputum_production == "Yes", 1L,0L),
                    Chronic_productive_cough = if_else(Chronic_productive_cough == "Yes", 1L, 0L),
                    Recurrent_wheeze = if_else(Recurrent_wheeze == "Yes", 1L,0L),
                    exp_dust_work = if_else(exp_dust_work == "Yes",1L,0L),
                    gender = if_else(gender == "male", 1L,0L))
  return(Airway2)
}

## Density plot
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(clusters)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 
```


# UFT + DEC method visulization
```{r}
Airway2 <- Get_Binary_data(mydata)
result_Air_tree_UFT_DEC <- Airway2
result_Air_tree_UFT_DEC$clusters <- result_airway_uft_DEC$clusters
#result_Air_tree_UFT_DEC <- result_Air_tree_UFT_DEC %>% mutate_if(is.integer, factor)
```
### Density plot shoiwing the age distribution for each cluster
```{r}
#result_Airway_tree$age <- mydata$age_class3 # adding age variable from the original data
#ggplot(data = result_Airway_tree, aes (x = result_Airway_tree$age, fill = result_Airway_tree$clusters )) +  geom_bar(position = "dodge2")
result_Air_tree_UFT_DEC$clusters <- as.factor(result_Air_tree_UFT_DEC$clusters)

age_g <- ggplot(result_Air_tree_UFT_DEC, aes(F107))
age_p <- age_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="Age of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="Age",
         fill="# Clusters")

#p <- ggplot(data = result_Airway_tree, aes (x = F107, fill = clusters )) +  geom_bar(position = "dodge2")
ggplotly(age_p)
```

```{r}
bmi_p <- dist_plot_clust(original_data = result_Air_tree_UFT_DEC, selected_variable = BMI)
bmi_p
```


# The tree-based distance approach



```{r}
plot_grid(age_p, bmi_p, labels = "auto")
```

```{r}
fev_g <- ggplot(result_Air_tree_UFT_DEC, aes(fev1pp_olin))
fev_p <- fev_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="fev1pp_olin of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="fev1pp_olin",
         fill="# Clusters")

fvc_g <- ggplot(result_Air_tree_UFT_DEC, aes(fev1pp_olin))
fvc_p <- fvc_g + geom_density(aes(fill=factor(clusters)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="fev1pp_olin of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="fev1pp_olin",
         fill="# Clusters")


plot_grid(fev_p, fvc_p, labels = "auto")
```

```{r}
fev_p <- dist_plot_clust(original_data = result_Air_tree_UFT_DEC, selected_variable = fev1pp_olin)
fvc_p <- dist_plot_clust(original_data = result_Air_tree_UFT_DEC, selected_variable = fev1pp_olin)
plot_grid(fev_p, fvc_p, labels = "auto")
```

### Histogram plots

```{r}
# Histogram on a Continuous (Numeric) Variable
g <- ggplot(result_Air_tree_UFT_DEC, aes(F107)) + scale_fill_brewer(palette = "Spectral")

k <- g + geom_histogram(aes(fill=factor(clusters)), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters")  
ggplotly(k)
```

```{r}
s <- g + geom_histogram(aes(fill=factor(clusters)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 

ggplotly(s)
```

<!-- ```{r} -->
<!-- # Plot -->
<!-- g <- ggplot(result_Airway_tree, aes(factor(clusters), F107)) -->
<!-- g + geom_boxplot(varwidth=T, fill="plum") +  -->
<!--     labs(title="Box plot",  -->
<!--          subtitle="City Mileage grouped by Class of vehicle", -->
<!--          caption="Source: mpg", -->
<!--          x="Clusters", -->
<!--          y="Age") -->
<!-- ``` -->


### Box plot

```{r}

g <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), F107))
l <- g + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="Age")
ggplotly(l)
```

```{r}
colnames(num_result_dec)
```

```{r}
g_2 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), fev1pp_olin))
l_2 <- g_2 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="fev1pp_olin")
ggplotly(l_2)
```
```{r}
g_3 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), BMI))
l_3 <- g_3 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="BMI")
ggplotly(l_3)
```

```{r}
g_4 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), post_fev1_fvc_ratio))
l_4 <- g_4 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="post_fev1_fvc_ratio")
ggplotly(l_4)
```

```{r}
g_5 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), delta_fev1))
l_5 <- g_5 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="delta_fev1")
ggplotly(l_5)
```

```{r}
g_6 <- ggplot(result_Air_tree_UFT_DEC, aes(factor(clusters), fvcpp_olin))
l_6 <- g_6 + geom_boxplot(aes(fill=factor(clusters))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="cluster of person",
       y="fvcpp_olin")
ggplotly(l_6)
```

## Box plot stratified by categorical variables
```{r}
ggplot(data = result_Air_tree_UFT_DEC,aes(x=as.factor(result_Air_tree_UFT_DEC$ever_smoker20py),y=F107,fill=clusters))+geom_boxplot()
```

## categorical variables vis
```{r}
# categorical variables vis
k <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$Longstanding_cough), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k) 
```

```{r}
k_2 <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$dyspneaMRC), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k_2) 
```

```{r}
k_3 <- ggplot(result_Air_tree_UFT_DEC, aes_string(as.factor(result_Air_tree_UFT_DEC$ever_smoker20py), fill = as.factor(result_Air_tree_UFT_DEC$clusters))) +  geom_bar(position = "dodge2") +
labs(title="Box plot", 
       subtitle="Age grouped by cluster of person",
       caption="Source: results of Hierarchical clustering with tree-based distance and distance d1",
       x="Long standing cough",
       y="Count")
ggplotly(k_3) 
```

## parallel box plots
```{r}
# google search : parallel box plot ggplot
# selecte the numerical variables
str(result_Air_tree_UFT_DEC)
result_Air_tree_UFT_DEC_2 <- result_Air_tree_UFT_DEC %>% mutate_if(is.integer, factor)
str(result_Air_tree_UFT_DEC_2)
num_result_dec <- result_Air_tree_UFT_DEC_2 %>% select_if(is.numeric)
colnames(num_result_dec)
library(reshape2)
num_result_dec$clusters <- result_Air_tree_UFT_DEC$clusters
num_result_dec_melt <- melt(num_result_dec, id.var = "clusters")
head(num_result_dec_melt)
ggplot(data = num_result_dec_melt, aes(x=variable, y=value)) + geom_boxplot(aes(fill=clusters))
```
```{r}
ggplot(data = num_result_dec_melt, aes(x=clusters, y=value)) + geom_boxplot(aes(fill=variable))
```

## parallel coordinate plot
```{r}
library(GGally)
library(ggplot2)

# Plot
head(num_result_dec)
ggparcoord(num_result_dec, columns = 1:6, groupColumn = 7) 
library(hrbrthemes)
library(viridis)
# std, uniminmax, center
ggparcoord(num_result_dec[500:900,],
    columns = 2:4, groupColumn = 7,scale = 'std', order = "anyClass",
    showPoints = TRUE, 
    title = "Parallel Coordinate Plot for the Iris Data",
    alphaLines = 0.3
    ) + 
  scale_color_viridis(discrete=TRUE) 
  #theme_ipsum()+
  #theme(
  #  plot.title = element_text(size=10)
  #)
```

```{r}
result_Airway_tree$ever_smoker20py <- as.factor(result_Airway_tree$ever_smoker20py)
result_Airway_tree$dyspneaMRC <- as.factor(result_Airway_tree$dyspneaMRC)
table_tree <- tableby(clusters ~ ., data = as.list(result_Airway_tree))
summary(table_tree, title = "Charachtaristcs of Clusters")  
write.csv(result_Airway_tree,'/Users/xbasra/Documents/Data/Airway_Clustering/Intermediate/CSV_output_data/result_Airway_tree_d1_hc.csv')
```


### Charachtiristic Analysis
```{r, warning=FALSE}
result_Air_tree_UFT_DEC$Labels <- as.factor(resutls_airway_UFT_DL$Labels)
catdes(resutls_airway_UFT_DL, 23)
head(result_Air_tree_UFT_DEC_2)
library(FactoMineR)
catdes(result_Air_tree_UFT_DEC_2, 23)
```





<!-- ## 2.2 Second applying Deep learning after the UFT process -->

<!-- ### UMAP plot -->

<!-- ```{r} -->
<!-- converted_umap_df$Labels <- resutls_airway_UFT_DL$Labels -->
<!-- ggplot(aes(x = X, y = Y, color = as.factor(Labels)), data = converted_umap_df) + -->
<!--     geom_point() + scale_color_brewer(palette="Dark2") -->
<!-- ``` -->


<!-- ### Bar plot shoiwing the age distribution for each cluster -->
<!-- ```{r} -->
<!-- resutls_airway_UFT_DL$age <- mydata$age_class3 # adding age variable from the original data -->
<!-- resutls_airway_UFT_DL$Labels <- as.factor(resutls_airway_UFT_DL$Labels) # converting to factor -->
<!-- p <- ggplot(data = resutls_airway_UFT_DL, aes (x = age, fill = Labels )) +  geom_bar(position = "dodge2") -->
<!-- ggplotly(p) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- # using the deep learning results on the UFT method -->
<!-- grp_uft_DL <- resutls_airway_UFT_DL$Labels -->
<!-- resutls_airway_UFT_DL <- Airway_Data -->
<!-- resutls_airway_UFT_DL$Labels <- grp_uft_DL -->
<!-- resutls_airway_UFT_DL$ever_smoker20py <- as.factor(resutls_airway_UFT_DL$ever_smoker20py) -->
<!-- resutls_airway_UFT_DL$dyspneaMRC <- as.factor(resutls_airway_UFT_DL$dyspneaMRC) -->
<!-- table_DL <- tableby(Labels ~ ., data = as.list(resutls_airway_UFT_DL)) -->
<!-- summary(table_DL, title = "Charachtaristcs of Clusters")   -->
<!-- #write.csv(resutls_airway_UFT_DL,'/Users/xbasra/Documents/Data/Clustering/Results_Data_Reports/CsvData/result_airway_UFT.csv') -->
<!-- ``` -->


<!-- ### Charachtiristic Analysis -->
<!-- ```{r, warning=FALSE} -->
<!-- resutls_airway_UFT_DL$Labels <- as.factor(resutls_airway_UFT_DL$Labels) -->
<!-- catdes(resutls_airway_UFT_DL, 23) -->
<!-- ``` -->









